Maximum likelihood-based extended Kalman filter for COVID-19 prediction

被引：26

作者：

Song, Jialu ^{[1
]}

Xie, Hujin ^{[1
]}

Gao, Bingbing ^{[2
]}

Zhong, Yongmin ^{[1
]}

Gu, Chengfan ^{[3
]}

Choi, Kup-Sze ^{[3
]}

机构：

[1] RMIT Univ, Sch Engn, Melbourne, Vic 3000, Australia

[2] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China

[3] Hong Kong Polytech Univ, Ctr Smart Hlth, Sch Nursing, Hong Kong, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2021年 / 146卷

关键词：

COVID-19; modelling; Extended Kalman filter; SEIRD model; Maximum likelihood estimation; Time-dependent model parameters; MODEL; CHINA;

D O I：

10.1016/j.chaos.2021.110922

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Prediction of COVID-19 spread plays a significant role in the epidemiology study and government battles against the epidemic. However, the existing studies on COVID-19 prediction are dominated by constant model parameters, unable to reflect the actual situation of COVID-19 spread. This paper presents a new method for dynamic prediction of COVID-19 spread by considering time-dependent model parameters. This method discretises the susceptible-exposed-infected-recovered-dead (SEIRD) epidemiological model in time domain to construct the nonlinear state-space equation for dynamic estimation of COVID19 spread. A maximum likelihood estimation theory is established to online estimate time-dependent model parameters. Subsequently, an extended Kalman filter is developed to estimate dynamic COVID-19 spread based on the online estimated model parameters. The proposed method is applied to simulate and analyse the COVID-19 pandemics in China and the United States based on daily reported cases, demonstrating its efficacy in modelling and prediction of COVID-19 spread. (c) 2021 Elsevier Ltd. All rights reserved.

引用

页数：9

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